In the field of product manufacturing, the term assemble-to-order (ATO) refers to systems where products are not produced until the demand for the products becomes known.
Manufacture-to-stock (MTS) systems, on the other hand, base production on component availability. Unlike ATO manufacturing, MTS systems often produce products before the demand for those products has been determined.
Manufacturing companies typically prefer to use ATO manufacturing operations whenever possible. ATO allows manufacturers to minimize their component inventories. This reduces the amount of capital invested in components and reduces the risk that components will loose value or become obsolete before they are transformed and sold as products.
In ATO manufacturing operations, it is paramount for decision-makers to be able to accurately forecast production on a period-by-period basis. The absence of accurate forecasting means that makes the estimation of important quantities such as expected revenues and costs difficult and in some cases impossible.
Unfortunately, accurately forecasting production can be problematic. Product demand is necessarily an important factor in estimations of this type. At the same time, product demand is not, by itself, a sufficient basis for forecasting. This follows because production can (and often is) constrained by the availability of the components needed to assemble the products. As a result, there is always a chance that the demand for a product will not be met within a particular period. Typically, this means that customers are either turned away or that orders are delayed—the product is “back ordered.”
To accurately forecast production, manufactures must be able to compute expected production from stochastic demand data, component consumption data and component levels. In many cases, this computation will involve a plurality of product and component kinds, potentially numbering in the thousands or more. FIG. 1 describes this problem for a simplified case where there are two products P1 and P2 and two components C1 and C2.
In FIG. 1, the production constraints imposed by the components C1 and C2 are shown as two dashed lines labeled C1 and C2. These lines divide the space of possible production values for the pair (P1, P2) into feasible and infeasible regions. The feasible region is the portion of the positive quadrant under the lines C1 and C2.
The probability distribution of demand is illustrated by the concentric ellipses. Each ellipse is an iso-probability curve (i.e. the line connects (P1, P2) points of equal probability). It is clear that there is a nonzero chance that the demand for the product pair may fall in the infeasible region.
If demand falls in the infeasible region, actual production will be less than demand. Hence mean production will be less than mean demand. There is a need for systems that can accurately calculate the differences between mean demand and mean production. This is particularly true for manufacturing operations that involve large numbers of products or large numbers of components. It is also particularly true where the markets for products or the market for components are volatile.